.png){kind=icon}
返回
Fully Bayesian Analysis of Relevance Vector Machine Classification With Probit Link Function for Imbalanced Data Problem
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3052935.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The original RVM classification model uses the logistic link function to build the likelihood function making the model hard to be conducted since the posterior of the weight parameter has no closed-form solution. This article proposes the probit link function approach instead of the logistic one for the likelihood function in the RVM classification model, namely PRVM (RVM with the probit link function). We show that the posterior of the weight parameter in PRVM follows the Multivariate Normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithms to simplify the Bayesian computation greatly, and its conditional posterior follows a truncated Normal distribution. Compared with the original RVM classification model, our proposed one is a Fully Bayesian approach, and it has a more efficient computation process. For the prior structure, we first consider the Normal-Gamma independent prior to propose a Generic Bayesian PRVM algorithm. Furthermore, the Fully Bayesian PRVM algorithm with a hierarchical hyperprior structure is proposed, which improves the classification performance, especially in the imbalanced data problem.
Keyword:
Support vector machines
Bayes methods
Logistics
Kernel
Training data
Training
Computational modeling
Bayesian analysis
imbalanced data problem
probit link function
RVM classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Driving Fatigue Classification Based on Fusion Entropy Analysis Combining EOG and EEG基于EOG和EEG融合熵分析的驾驶乏力分类.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Multivariate Seawater Quality Prediction Based on PCA-RVM Supported by Edge Computing Towards Smart OceanIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容